Belief revision and text mining for adaptive recommender agents

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)226-230
Journal / PublicationLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Publication statusPublished - 2003
Externally publishedYes


Title14th International Symposium, ISMIS 2003
CityMaebashi City
Period28 - 31 October 2003


With the rapid growth, of the number of electronic transactions conducted over the Internet, recommander systems have been proposed to provide consumers with personalized product recommendations. This paper illustrates how belief revision and text mining can be used to improve recommender agents' prediction effectiveness, learning autonomy, adaptiveness, and explanatory capabilities. To our best knowledge, this is the first study of integrating text mining techniques and belief revision logic into a single framework for the development of adaptive recommender agents.

Research Area(s)

  • Belief Revision, Recommender Agents, Text Mining